get_showGet one season's details: titles, network, status, air dates, reunion date, tracking window (trackingStart/trackingEnd), coverageNote, episode count.
| Name | Required | Description | Default |
|---|---|---|---|
| show | Yes | Season ID or slug, e.g. "12" or "love-island-usa-season-8". Use list_shows to discover. |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With readOnlyHint already covering non-mutating behavior, the description adds useful transparency about what the response contains (titles, network, status, air dates, trackingStart/trackingEnd, coverageNote, episode count). It does not mention edge-case behavior like invalid IDs or response shape, but for a simple read it is adequate.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
A single front-loaded sentence communicates the operation, scope, and explicit payload fields with no filler. It earns its place entirely.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a one-parameter read-only tool with no output schema, the description covers what will be returned and the schema covers the input. No critical information an agent needs to call it correctly is missing.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% and the schema's 'show' parameter already includes a clear format, an example, and a discovery hint. The tool description adds no additional parameter meaning, so the baseline of 3 applies.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb ('Get'), names the exact resource ('one season's details'), and enumerates the distinguishing fields (network, air dates, reunion date, tracking window, episode count), which separates it clearly from sibling tools like get_cast or get_follower_history.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is given about when to choose this tool over alternatives or when not to use it. The only related hint, 'Use list_shows to discover,' appears in the parameter schema and addresses ID discovery, not tool selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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Each tool targets a distinct resource or analysis need: cast summaries, single-contestant details, follower time series, follow/unfollow events, follow graph, trend events, and export output are cleanly separated. The descriptions explicitly cross-reference related tools, so an agent should be able to pick the right one without ambiguity.
Tool names follow a consistent get_/noun and list_noun pattern, with export_season_csv as the only slight variation—but it still clearly uses verb_noun convention. camelCase is avoided, and duplicate or vague verbs are absent.
Twelve tools is a well-scoped size for a read-only analytics data API. Each tool contributes a meaningful slice of the domain—discovery, show details, cast metrics, raw series, events, graphs, trends, usage, and export—without redundancy or bloat.
The surface covers the full read-only workflow: discover shows and seasons, list episodes, inspect contestants and cast, retrieve follower histories, engagement, follow relationships, trend events, and export a citation-ready CSV. No obvious lifecycle dead ends exist since the API is inherently data-access-oriented rather than CRUD.